Method And System For Reconstructing Image Of Midpalatal Suture Based On Sobel Operator

Abstract

The present disclosure relates to a method and system for reconstructing an image of a midpalatal suture based on a Sobel operator and belongs to the technical field of image processing. Firstly, data preprocessing is performed on a cone beam computed tomography (CBCT) file of a midpalatal suture of a maxilla to obtain a plurality of local images of the midpalatal suture. Merging fusion is then performed on the plurality of local images of the midpalatal suture to obtain a reconstructed image of the midpalatal suture, and a Sobel operator is utilized for optimization during the merging fusion. Thus, an overall profile of a region shape of the midpalatal suture can be reconstructed in a same image from midpalatal suture regions distributed in multiple layers of CBCT images, facilitating intuitive and accurate judgment on the midpalatal suture region by a doctor.

What is claimed is: 
     
         1 . A method for reconstructing an image of a midpalatal suture based on a Sobel operator, comprising the following steps:
 performing data preprocessing on a cone beam computed tomography (CBCT) file of a midpalatal suture of a maxilla to obtain a plurality of local images of the midpalatal suture; and   performing merging fusion on the plurality of local images of the midpalatal suture to obtain a reconstructed image of the midpalatal suture, wherein a Sobel operator is utilized for optimization during the merging fusion.   
     
     
         2 . The method according to  claim 1 , wherein the performing data preprocessing on a CBCT file of a midpalatal suture of a maxilla to obtain a plurality of local images of the midpalatal suture specifically comprises the following steps:
 reading the CBCT file of the midpalatal suture of the maxilla to obtain file information, wherein the file information comprises a resolution, a number of layers, a window width, and a window level of the CBCT file of the midpalatal suture of the maxilla;   transforming the CBCT file of the midpalatal suture of the maxilla into a three-dimensional gray-level matrix based on the file information, and transforming the three-dimensional gray-level matrix into a plurality of axial CT cross-sectional images; and   cutting each of the axial CT cross-sectional images containing midpalatal suture regions to obtain the plurality of local images of the midpalatal suture.   
     
     
         3 . The method according to  claim 2 , wherein the transforming the CBCT file of the midpalatal suture of the maxilla into a three-dimensional gray-level matrix based on the file information specifically comprises the following steps:
 determining a three-dimensional size of the three-dimensional gray-level matrix based on the resolution and the number of layers;   determining a gray level interval of the three-dimensional gray-level matrix based on the window width and the window level; and   transforming the CBCT file of the midpalatal suture of the maxilla into the three-dimensional gray-level matrix based on the three-dimensional size and the gray level interval.   
     
     
         4 . The method according to  claim 1 , wherein the performing merging fusion on the plurality of local images of the midpalatal suture to obtain a reconstructed image of the midpalatal suture specifically comprises the following steps:
 combining the plurality of local images of the midpalatal suture in pairs to obtain a plurality of image pairs to be fused, wherein pixels of the local images of the midpalatal suture comprised in each image pair to be fused are in one-to-one correspondence with each other, and two pixels in the one-to-one correspondence are denoted as a pixel pair;   for each image pair to be fused, calculating a fusion weight for each pixel pair, and performing merging fusion on the image pair to be fused based on the fusion weight for each pixel pair to obtain a fused image;   optimizing each fused image with the Sobel operator to obtain an optimized image;   determining whether a number of the optimized images is 1;   if yes, taking the optimized image as the reconstructed image of the midpalatal suture; and   if no, taking the optimized images as the local images of the midpalatal suture in next loop, and returning to the step of combining the plurality of local images of the midpalatal suture in pairs.   
     
     
         5 . The method according to  claim 4 , wherein the calculating a fusion weight for each pixel pair specifically comprises the following steps:
 calculating an overall average gray level and an adjustment factor based on gray level values of the plurality of local images of the midpalatal suture;   for each pixel pair, calculating an average value of gray level values of the image pair to be fused at the pixel pair to obtain an average gray level; and   calculating the fusion weight for the pixel pair based on the overall average gray level, the adjustment factor, and the average gray level.   
     
     
         6 . The method according to  claim 4 , wherein the performing merging fusion on the image pair to be fused based on the fusion weight for each pixel pair to obtain a fused image specifically comprises the following steps:
 for each pixel pair, calculating an average value of gray level values of the image pair to be fused at the pixel pair to obtain an average gray level; and   calculating a fused gray level value of the pixel pair based on the average gray level and the fusion weight to obtain the fused image.   
     
     
         7 . The method according to  claim 4 , wherein the optimizing each fused image with the Sobel operator to obtain an optimized image specifically comprises the following step:
 for each pixel of each fused image, calculating an optimized gray level value of the pixel based on the Sobel operator to obtain the optimized image.   
     
     
         8 . The method according to  claim 1 , further comprising the following steps after obtaining the reconstructed image of the midpalatal suture:
 analyzing a texture feature of the reconstructed image of the midpalatal suture to adjust a weight for the Sobel operator, thereby obtaining an optimized Sobel operator;   performing merging fusion on the plurality of local images of the midpalatal suture to obtain a new reconstructed image of the midpalatal suture, wherein the optimized Sobel operator is utilized for optimization during the merging fusion;   determining whether a maximum number of iterations is reached;   if yes, determining an optimal Sobel operator based on optimization of all reconstructed images, wherein the reconstructed images comprise the reconstructed image of the midpalatal suture and the new reconstructed image of the midpalatal suture; and   if no, taking the new reconstructed image of the midpalatal suture as the reconstructed image of the midpalatal suture in next loop and the optimized Sobel operator as the Sobel operator in next loop, and returning to the step of analyzing a texture feature of the reconstructed image of the midpalatal suture.   
     
     
         9 . A system for reconstructing an image of a midpalatal suture based on a Sobel operator, comprising:
 a data preprocessing module configured to perform data preprocessing on a CBCT file of a midpalatal suture of a maxilla to obtain a plurality of local images of the midpalatal suture; and   a fusing module configured to perform merging fusion on the plurality of local images of the midpalatal suture to obtain a reconstructed image of the midpalatal suture, wherein a Sobel operator is utilized for optimization during the merging fusion.   
     
     
         10 . The system according to  claim 9 , wherein the fusing module specifically comprises:
 a combining unit configured to combine the plurality of local images of the midpalatal suture in pairs to obtain a plurality of image pairs to be fused, wherein pixels of the local images of the midpalatal suture comprised in each image pair to be fused correspond to one another one to one, and two pixels in the one-to-one correspondence are denoted as a pixel pair;   a fusing unit configured to, for each image pair to be fused, calculate a fusion weight for each pixel pair, and perform merging fusion on the image pair to be fused based on the fusion weight for each pixel pair to obtain a fused image;   an optimizing unit configured to optimize each fused image with the Sobel operator to obtain an optimized image;   a determining unit configured to determine whether a number of the optimized images is 1;   a reconstructing unit configured to, if yes, take the optimized image as the reconstructed image of the midpalatal suture; and   a returning unit configured to, if no, take the optimized images as the local images of the midpalatal suture in next loop, and return to the step of combining the plurality of local images of the midpalatal suture in pairs.